Here, we’re just setting a few options.

knitr::opts_chunk$set(
  warning = TRUE, # show warnings during codebook generation
  message = TRUE, # show messages during codebook generation
  error = TRUE, # do not interrupt codebook generation in case of errors,
                # usually better for debugging
  echo = TRUE  # show R code
)
ggplot2::theme_set(ggplot2::theme_bw())
pander::panderOptions("table.split.table", Inf)

Now, we’re preparing our data for the codebook.

library(codebook)
codebook_data <- codebook::bfi
# to import an SPSS file from the same folder uncomment and edit the line below
# codebook_data <- rio::import("mydata.sav")
# for Stata
# codebook_data <- rio::import("mydata.dta")
# for CSV
# codebook_data <- rio::import("mydata.csv")

# omit the following lines, if your missing values are already properly labelled
codebook_data <- detect_missing(codebook_data,
    only_labelled = TRUE, # only labelled values are autodetected as
                                   # missing
    negative_values_are_missing = FALSE, # negative values are missing values
    ninety_nine_problems = TRUE,   # 99/999 are missing values, if they
                                   # are more than 5 MAD from the median
    )

# If you are not using formr, the codebook package needs to guess which items
# form a scale. The following line finds item aggregates with names like this:
# scale = scale_1 + scale_2R + scale_3R
# identifying these aggregates allows the codebook function to
# automatically compute reliabilities.
# However, it will not reverse items automatically.
codebook_data <- detect_scales(codebook_data)
## 4 BFIK_open items connected to scale
## 4 BFIK_agree items connected to scale
## 4 BFIK_extra items connected to scale
## 3 BFIK_neuro items connected to scale
## 4 BFIK_consc items connected to scale

Create codebook

codebook(codebook_data)
## Warning in doTryCatch(return(expr), name, parentenv, handler): Reliability CIs
## could not be computed for BFIK_open
## Warning in doTryCatch(return(expr), name, parentenv, handler): missing value
## where TRUE/FALSE needed
## Warning in value[[3L]](cond): Reliability could not be computed for BFIK_open
## Warning in value[[3L]](cond): missing value where TRUE/FALSE needed
## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2
## Registered S3 methods overwritten by 'lme4':
##   method                          from
##   cooks.distance.influence.merMod car 
##   influence.merMod                car 
##   dfbeta.influence.merMod         car 
##   dfbetas.influence.merMod        car
knitr::asis_output(data_info)

Metadata

Description

if (exists("name", meta)) {
  glue::glue(
    "__Dataset name__: {name}",
    .envir = meta)
}

Dataset name: codebook_data

cat(description)

The dataset has N=28 rows and 29 columns. 0 rows have no missing values on any column.

Metadata for search engines

  • Date published: 2020-05-02
meta <- meta[setdiff(names(meta),
                     c("creator", "datePublished", "identifier",
                       "url", "citation", "spatialCoverage", 
                       "temporalCoverage", "description", "name"))]
pander::pander(meta)
  • keywords: session, created, modified, ended, expired, BFIK_open_2, BFIK_agree_4R, BFIK_extra_2, BFIK_agree_1R, BFIK_open_1, BFIK_neuro_2R, BFIK_consc_3, BFIK_consc_4, BFIK_consc_2R, BFIK_agree_3R, BFIK_extra_3R, BFIK_neuro_3, BFIK_neuro_4, BFIK_agree_2, BFIK_consc_1, BFIK_open_4, BFIK_extra_4, BFIK_extra_1R, BFIK_open_3, BFIK_agree, BFIK_open, BFIK_consc, BFIK_extra and BFIK_neuro
knitr::asis_output(survey_overview)

Survey overview

28 completed rows, 28 who entered any information, 0 only viewed the first page. There are 0 expired rows (people who did not finish filling out in the requested time frame). In total, there are 28 rows including unfinished and expired rows.

There were 28 unique participants, of which 28 finished filling out at least one survey.

This survey was not repeated.

if (survey_repetition != "single") {
    overview = results %>% dplyr::group_by(session) %>% 
        dplyr::summarise(
            n = sum(!is.na(session)),
            expired = sum(!is.na(expired)),
            ended = sum(!is.na(ended))
        ) %>% 
        tidyr::gather(key, value, -session)
    if (length(unique(dplyr::filter(overview, key == "expired")$value)) == 1) {
        overview = dplyr::filter(overview, key != "expired")
    }
    print(
        ggplot2::ggplot(overview, ggplot2::aes(value, ..count..)) + ggplot2::geom_bar() + ggplot2::facet_wrap(~ key, nrow = 1)
    )
}

The first session started on 2016-07-08 13:24:16, the last session on 2016-11-03 01:49:50.

ggplot2::qplot(results$created) + ggplot2::scale_x_datetime("Date/time when survey was started")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

People took on average 127.36 minutes (median 1.48) to answer the survey.

if (low_vals == 0) {
  warning("Durations below 0 detected.")
}
## Warning: Durations below 0 detected.
ggplot2::qplot(duration$duration, binwidth = 0.5) + ggplot2::scale_x_continuous(paste("Duration (in minutes), excluding", high_vals, "values above median + 4*MAD"), limits = c(lower_limit, upper_limit))
## Warning: Removed 4 rows containing non-finite values (stat_bin).
## Warning: Removed 2 rows containing missing values (geom_bar).

Variables

if (detailed_variables || detailed_scales) {
  knitr::asis_output(paste0(scales_items, sep = "\n\n\n", collapse = "\n\n\n"))
}

Scale: BFIK_agree

Overview

Reliability: ωordinal [95% CI] = 0.61 [0.37;0.84].

Missing: 0.

old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height), 
                     old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)
if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
  likert_plot <- likert_from_items(items)
  if (!is.null(likert_plot)) {
    graphics::plot(likert_plot)
  }
}

knitr::opts_chunk$set(fig.height = old_height)
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
dist_plot <- plot_labelled(scale, scale_name, wrap_at)

choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
  suppressMessages( # ignore message about overwriting x axis
  dist_plot <- dist_plot +
        ggplot2::scale_x_continuous("values", 
                                breaks = breaks, 
                                labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
      ggplot2::expand_limits(x = range(breaks)))
  
}

dist_plot

Reliability details

for (i in seq_along(reliabilities)) {
  rel <- reliabilities[[i]]
  cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}
Reliability Indices
coefs <- x$scaleReliability$output$dat %>% 
  tidyr::gather(index, estimate) %>% 
  dplyr::filter(index != "n.items", index != "n.observations") %>% 
  dplyr::mutate(index = stringr::str_to_title(
    stringr::str_replace_all(index,
      stringr::fixed("."), " ")))

cis <- coefs %>% 
  dplyr::filter(stringr::str_detect(index, " Ci ")) %>% 
  tidyr::separate(index, c("index", "hilo"), sep = " Ci ") %>% 
  tidyr::spread(hilo, estimate)
if (nrow(cis)) {
  cis <- cis %>% dplyr::rename(
    `Lower 95% CI` = .data$Lo, `Upper 95% CI` = .data$Hi
  )
}

coefs_with_cis <- coefs %>% 
  dplyr::filter(!stringr::str_detect(index, " Ci ")) %>% 
    dplyr::left_join(cis, by = "index") %>% 
    dplyr::mutate(index = dplyr::if_else(index == "Glb", "Greatest Lower Bound", .data$index)) %>% 
    dplyr::arrange(!stringr::str_detect(index, "Omega")) %>% 
    dplyr::select(Index = .data$index, Estimate = .data$estimate)


pander::pander(coefs_with_cis)
Index Estimate
Omega 0.8193
Omega Psych Tot 0.8878
Omega Psych H 0.7664
Omega Ordinal 0.605
Cronbach Alpha 0.8006
Greatest Lower Bound 0.8858
Alpha Ordinal 0.5879

Positive correlations: 6 out of 6 (100%)

Scatter matrix
print(x$scatterMatrix$output$scatterMatrix)

x$scatterMatrix$output$scatterMatrix <- no_md()

Detailed output

print(x)
## 
## Information about this analysis:
## 
##                  Dataframe: res$dat
##                      Items: BFIK_agree_4R, BFIK_agree_1R, BFIK_agree_3R, BFIK_agree_2
##               Observations: 28
##      Positive correlations: 6 out of 6 (100%)
## 
## Estimates assuming interval level:
## 
##              Omega (total): 0.82
##       Omega (hierarchical): 0.77
##    Revelle's omega (total): 0.89
## Greatest Lower Bound (GLB): 0.89
##              Coefficient H: 0.88
##           Cronbach's alpha: 0.8
## Confidence intervals:
##              Omega (total): [0.71, 0.93]
##           Cronbach's alpha: [0.68, 0.92]
## 
## Estimates assuming ordinal level:
## 
##      Ordinal Omega (total): 0.61
##  Ordinal Omega (hierarch.): 0.59
##   Ordinal Cronbach's alpha: 0.59
## Confidence intervals:
##      Ordinal Omega (total): [0.37, 0.84]
##   Ordinal Cronbach's alpha: [0.33, 0.84]
## 
## Note: the normal point estimate and confidence interval for omega are based on the procedure suggested by Dunn, Baguley & Brunsden (2013) using the MBESS function ci.reliability, whereas the psych package point estimate was suggested in Revelle & Zinbarg (2008). See the help ('?scaleStructure') for more information.
## 
## Eigen values: 2.539, 0.732, 0.54, 0.189
## Loadings:
##               PC1  
## BFIK_agree_4R 0.871
## BFIK_agree_1R 0.748
## BFIK_agree_3R 0.880
## BFIK_agree_2  0.668
## 
##                  PC1
## SS loadings    2.539
## Proportion Var 0.635
## 
##               vars  n mean   sd median trimmed  mad min max range  skew
## BFIK_agree_4R    1 28 2.93 1.18      3    2.92 1.48   1   5     4  0.26
## BFIK_agree_1R    2 28 3.00 0.94      3    2.96 1.48   2   5     3  0.26
## BFIK_agree_3R    3 28 3.04 1.29      3    3.04 1.48   1   5     4  0.04
## BFIK_agree_2     4 28 3.50 1.26      4    3.58 1.48   1   5     4 -0.43
##               kurtosis   se
## BFIK_agree_4R    -1.18 0.22
## BFIK_agree_1R    -1.37 0.18
## BFIK_agree_3R    -1.35 0.24
## BFIK_agree_2     -1.03 0.24

Summary statistics

for (i in seq_along(names(items))) {
  attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))
name label type type_options data_type value_labels optional item_order n_missing complete_rate min median max mean sd n_value_labels hist
BFIK_agree_4R Ich kann mich schroff und abweisend anderen gegenüber verhalten. rating_button 5 haven_labelled 5. 1: Trifft überhaupt nicht zu,
4. 2,
3. 3,
2. 4,
1. 5: Trifft voll und ganz zu,
NA. Item was never rendered for this user.		 0 5 0 1 1 3 5 2.928571 1.184110 6 ▂▇▁▃▁▅▁▂
BFIK_agree_1R Ich neige dazu, andere zu kritisieren. rating_button 5 haven_labelled 5. 1: Trifft überhaupt nicht zu,
4. 2,
3. 3,
2. 4,
1. 5: Trifft voll und ganz zu,
NA. Item was never rendered for this user.		 0 7 0 1 2 3 5 3.000000 0.942809 6 ▇▁▅▁▁▆▁▁
BFIK_agree_3R Ich kann mich kalt und distanziert verhalten. rating_button 5 haven_labelled 5. 1: Trifft überhaupt nicht zu,
4. 2,
3. 3,
2. 4,
1. 5: Trifft voll und ganz zu,
NA. Item was never rendered for this user.		 0 13 0 1 1 3 5 3.035714 1.290482 6 ▂▇▁▃▁▇▁▃
BFIK_agree_2 Ich schenke anderen leicht Vertrauen, glaube an das Gute im Menschen. rating_button 5 haven_labelled 1. 1: Trifft überhaupt nicht zu,
2. 2,
3. 3,
4. 4,
5. 5: Trifft voll und ganz zu,
NA. Item was never rendered for this user.		 0 17 0 1 1 4 5 3.500000 1.261980 6 ▂▅▁▅▁▇▁▆

Scale: BFIK_open

Overview

Reliability: Not computed.

Missing: 0.

old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height), 
                     old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)
if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
  likert_plot <- likert_from_items(items)
  if (!is.null(likert_plot)) {
    graphics::plot(likert_plot)
  }
}

knitr::opts_chunk$set(fig.height = old_height)
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
dist_plot <- plot_labelled(scale, scale_name, wrap_at)

choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
  suppressMessages( # ignore message about overwriting x axis
  dist_plot <- dist_plot +
        ggplot2::scale_x_continuous("values", 
                                breaks = breaks, 
                                labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
      ggplot2::expand_limits(x = range(breaks)))
  
}

dist_plot

Reliability details

for (i in seq_along(reliabilities)) {
  rel <- reliabilities[[i]]
  cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}

Summary statistics

for (i in seq_along(names(items))) {
  attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))
name label type type_options data_type value_labels optional item_order n_missing complete_rate min median max mean sd n_value_labels hist
BFIK_open_2 Ich bin tiefsinnig, denke gerne über Sachen nach. rating_button 5 haven_labelled 1. 1: Trifft überhaupt nicht zu,
2. 2,
3. 3,
4. 4,
5. 5: Trifft voll und ganz zu,
NA. Item was never rendered for this user.		 0 4 0 1 2 4.0 5 4.214286 0.7382232 6 ▁▁▁▁▁▇▁▅
BFIK_open_1 Ich bin vielseitig interessiert. rating_button 5 haven_labelled 1. 1: Trifft überhaupt nicht zu,
2. 2,
3. 3,
4. 4,
5. 5: Trifft voll und ganz zu,
NA. Item was never rendered for this user.		 0 8 0 1 2 5.0 5 4.392857 0.8317445 6 ▁▁▂▁▁▃▁▇
BFIK_open_4 Ich schätze künstlerische und ästhetische Eindrücke. rating_button 5 haven_labelled 1. 1: Trifft überhaupt nicht zu,
2. 2,
3. 3,
4. 4,
5. 5: Trifft voll und ganz zu,
NA. Item was never rendered for this user.		 0 19 0 1 1 4.0 5 4.214286 0.9567361 6 ▁▁▁▂▁▆▁▇
BFIK_open_3 Ich habe eine aktive Vorstellungskraft, bin phantasievoll. rating_button 5 haven_labelled 1. 1: Trifft überhaupt nicht zu,
2. 2,
3. 3,
4. 4,
5. 5: Trifft voll und ganz zu,
NA. Item was never rendered for this user.		 0 22 0 1 2 4.5 5 4.214286 0.9567361 6 ▁▁▂▁▁▅▁▇

Scale: BFIK_consc

Overview

Reliability: ωordinal [95% CI] = 0.61 [0.38;0.84].

Missing: 0.

old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height), 
                     old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)
if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
  likert_plot <- likert_from_items(items)
  if (!is.null(likert_plot)) {
    graphics::plot(likert_plot)
  }
}

knitr::opts_chunk$set(fig.height = old_height)
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
dist_plot <- plot_labelled(scale, scale_name, wrap_at)

choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
  suppressMessages( # ignore message about overwriting x axis
  dist_plot <- dist_plot +
        ggplot2::scale_x_continuous("values", 
                                breaks = breaks, 
                                labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
      ggplot2::expand_limits(x = range(breaks)))
  
}

dist_plot

Reliability details

for (i in seq_along(reliabilities)) {
  rel <- reliabilities[[i]]
  cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}
Reliability Indices
coefs <- x$scaleReliability$output$dat %>% 
  tidyr::gather(index, estimate) %>% 
  dplyr::filter(index != "n.items", index != "n.observations") %>% 
  dplyr::mutate(index = stringr::str_to_title(
    stringr::str_replace_all(index,
      stringr::fixed("."), " ")))

cis <- coefs %>% 
  dplyr::filter(stringr::str_detect(index, " Ci ")) %>% 
  tidyr::separate(index, c("index", "hilo"), sep = " Ci ") %>% 
  tidyr::spread(hilo, estimate)
if (nrow(cis)) {
  cis <- cis %>% dplyr::rename(
    `Lower 95% CI` = .data$Lo, `Upper 95% CI` = .data$Hi
  )
}

coefs_with_cis <- coefs %>% 
  dplyr::filter(!stringr::str_detect(index, " Ci ")) %>% 
    dplyr::left_join(cis, by = "index") %>% 
    dplyr::mutate(index = dplyr::if_else(index == "Glb", "Greatest Lower Bound", .data$index)) %>% 
    dplyr::arrange(!stringr::str_detect(index, "Omega")) %>% 
    dplyr::select(Index = .data$index, Estimate = .data$estimate)


pander::pander(coefs_with_cis)
Index Estimate
Omega 0.812
Omega Psych Tot 0.3688
Omega Psych H 0.2444
Omega Ordinal 0.6077
Cronbach Alpha 0.7797
Greatest Lower Bound 0.9018
Alpha Ordinal 0.5935

Positive correlations: 6 out of 6 (100%)

Scatter matrix
print(x$scatterMatrix$output$scatterMatrix)

x$scatterMatrix$output$scatterMatrix <- no_md()

Detailed output

print(x)
## 
## Information about this analysis:
## 
##                  Dataframe: res$dat
##                      Items: BFIK_consc_3, BFIK_consc_4, BFIK_consc_2R, BFIK_consc_1
##               Observations: 28
##      Positive correlations: 6 out of 6 (100%)
## 
## Estimates assuming interval level:
## 
##              Omega (total): 0.81
##       Omega (hierarchical): 0.24
##    Revelle's omega (total): 0.37
## Greatest Lower Bound (GLB): 0.9
##              Coefficient H: 1
##           Cronbach's alpha: 0.78
## Confidence intervals:
##              Omega (total): [0.7, 0.92]
##           Cronbach's alpha: [0.65, 0.91]
## 
## Estimates assuming ordinal level:
## 
##      Ordinal Omega (total): 0.61
##  Ordinal Omega (hierarch.): 0.59
##   Ordinal Cronbach's alpha: 0.59
## Confidence intervals:
##      Ordinal Omega (total): [0.38, 0.84]
##   Ordinal Cronbach's alpha: [0.34, 0.84]
## 
## Note: the normal point estimate and confidence interval for omega are based on the procedure suggested by Dunn, Baguley & Brunsden (2013) using the MBESS function ci.reliability, whereas the psych package point estimate was suggested in Revelle & Zinbarg (2008). See the help ('?scaleStructure') for more information.
## 
## Eigen values: 2.473, 0.777, 0.539, 0.211
## Loadings:
##               PC1  
## BFIK_consc_3  0.926
## BFIK_consc_4  0.738
## BFIK_consc_2R 0.800
## BFIK_consc_1  0.657
## 
##                  PC1
## SS loadings    2.473
## Proportion Var 0.618
## 
##               vars  n mean   sd median trimmed  mad min max range  skew
## BFIK_consc_3     1 28 3.50 1.04      4    3.54 1.48   1   5     4 -0.48
## BFIK_consc_4     2 28 3.86 0.76      4    3.88 0.00   2   5     3 -0.27
## BFIK_consc_2R    3 28 3.18 1.31      4    3.21 1.48   1   5     4 -0.51
## BFIK_consc_1     4 28 4.07 0.90      4    4.17 1.48   2   5     3 -0.72
##               kurtosis   se
## BFIK_consc_3     -0.50 0.20
## BFIK_consc_4     -0.35 0.14
## BFIK_consc_2R    -1.08 0.25
## BFIK_consc_1     -0.30 0.17

Summary statistics

for (i in seq_along(names(items))) {
  attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))
name label type type_options data_type value_labels optional item_order n_missing complete_rate min median max mean sd n_value_labels hist
BFIK_consc_3 Ich bin tüchtig und arbeite flott. rating_button 5 haven_labelled 1. 1: Trifft überhaupt nicht zu,
2. 2,
3. 3,
4. 4,
5. 5: Trifft voll und ganz zu,
NA. Item was never rendered for this user.		 0 10 0 1 1 4 5 3.500000 1.0363755 6 ▁▂▁▅▁▇▁▂
BFIK_consc_4 Ich mache Pläne und führe sie auch durch. rating_button 5 haven_labelled 1. 1: Trifft überhaupt nicht zu,
2. 2,
3. 3,
4. 4,
5. 5: Trifft voll und ganz zu,
NA. Item was never rendered for this user.		 0 11 0 1 2 4 5 3.857143 0.7559289 6 ▁▁▃▁▁▇▁▂
BFIK_consc_2R Ich bin bequem, neige zur Faulheit. rating_button 5 haven_labelled 5. 1: Trifft überhaupt nicht zu,
4. 2,
3. 3,
2. 4,
1. 5: Trifft voll und ganz zu,
NA. Item was never rendered for this user.		 0 12 0 1 1 4 5 3.178571 1.3067792 6 ▃▂▁▃▁▇▁▂
BFIK_consc_1 Ich erledige Aufgaben gründlich. rating_button 5 haven_labelled 1. 1: Trifft überhaupt nicht zu,
2. 2,
3. 3,
4. 4,
5. 5: Trifft voll und ganz zu,
NA. Item was never rendered for this user.		 0 18 0 1 2 4 5 4.071429 0.8997354 6 ▁▁▂▁▁▇▁▇

Scale: BFIK_extra

Overview

Reliability: ωordinal [95% CI] = 0.78 [0.64;0.91].

Missing: 0.

old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height), 
                     old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)
if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
  likert_plot <- likert_from_items(items)
  if (!is.null(likert_plot)) {
    graphics::plot(likert_plot)
  }
}

knitr::opts_chunk$set(fig.height = old_height)
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
dist_plot <- plot_labelled(scale, scale_name, wrap_at)

choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
  suppressMessages( # ignore message about overwriting x axis
  dist_plot <- dist_plot +
        ggplot2::scale_x_continuous("values", 
                                breaks = breaks, 
                                labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
      ggplot2::expand_limits(x = range(breaks)))
  
}

dist_plot

Reliability details

for (i in seq_along(reliabilities)) {
  rel <- reliabilities[[i]]
  cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}
Reliability Indices
coefs <- x$scaleReliability$output$dat %>% 
  tidyr::gather(index, estimate) %>% 
  dplyr::filter(index != "n.items", index != "n.observations") %>% 
  dplyr::mutate(index = stringr::str_to_title(
    stringr::str_replace_all(index,
      stringr::fixed("."), " ")))

cis <- coefs %>% 
  dplyr::filter(stringr::str_detect(index, " Ci ")) %>% 
  tidyr::separate(index, c("index", "hilo"), sep = " Ci ") %>% 
  tidyr::spread(hilo, estimate)
if (nrow(cis)) {
  cis <- cis %>% dplyr::rename(
    `Lower 95% CI` = .data$Lo, `Upper 95% CI` = .data$Hi
  )
}

coefs_with_cis <- coefs %>% 
  dplyr::filter(!stringr::str_detect(index, " Ci ")) %>% 
    dplyr::left_join(cis, by = "index") %>% 
    dplyr::mutate(index = dplyr::if_else(index == "Glb", "Greatest Lower Bound", .data$index)) %>% 
    dplyr::arrange(!stringr::str_detect(index, "Omega")) %>% 
    dplyr::select(Index = .data$index, Estimate = .data$estimate)


pander::pander(coefs_with_cis)
Index Estimate
Omega 0.9023
Omega Psych Tot 0.9589
Omega Psych H 0.8395
Omega Ordinal 0.775
Cronbach Alpha 0.8993
Greatest Lower Bound 0.9581
Alpha Ordinal 0.7744

Positive correlations: 6 out of 6 (100%)

Scatter matrix
print(x$scatterMatrix$output$scatterMatrix)

x$scatterMatrix$output$scatterMatrix <- no_md()

Detailed output

print(x)
## 
## Information about this analysis:
## 
##                  Dataframe: res$dat
##                      Items: BFIK_extra_2, BFIK_extra_3R, BFIK_extra_4, BFIK_extra_1R
##               Observations: 28
##      Positive correlations: 6 out of 6 (100%)
## 
## Estimates assuming interval level:
## 
##              Omega (total): 0.9
##       Omega (hierarchical): 0.84
##    Revelle's omega (total): 0.96
## Greatest Lower Bound (GLB): 0.96
##              Coefficient H: 0.93
##           Cronbach's alpha: 0.9
## Confidence intervals:
##              Omega (total): [0.84, 0.96]
##           Cronbach's alpha: [0.83, 0.96]
## 
## Estimates assuming ordinal level:
## 
##      Ordinal Omega (total): 0.78
##  Ordinal Omega (hierarch.): 0.75
##   Ordinal Cronbach's alpha: 0.77
## Confidence intervals:
##      Ordinal Omega (total): [0.64, 0.91]
##   Ordinal Cronbach's alpha: [0.64, 0.91]
## 
## Note: the normal point estimate and confidence interval for omega are based on the procedure suggested by Dunn, Baguley & Brunsden (2013) using the MBESS function ci.reliability, whereas the psych package point estimate was suggested in Revelle & Zinbarg (2008). See the help ('?scaleStructure') for more information.
## 
## Eigen values: 3.077, 0.527, 0.297, 0.099
## Loadings:
##               PC1  
## BFIK_extra_2  0.806
## BFIK_extra_3R 0.883
## BFIK_extra_4  0.908
## BFIK_extra_1R 0.907
## 
##                  PC1
## SS loadings    3.077
## Proportion Var 0.769
## 
##               vars  n mean   sd median trimmed  mad min max range  skew
## BFIK_extra_2     1 28 4.18 1.09      4    4.38 1.48   1   5     4 -1.66
## BFIK_extra_3R    2 28 3.75 1.21      4    3.88 1.48   1   5     4 -0.76
## BFIK_extra_4     3 28 3.86 1.11      4    3.96 1.48   1   5     4 -0.82
## BFIK_extra_1R    4 28 3.61 1.20      4    3.67 1.48   1   5     4 -0.37
##               kurtosis   se
## BFIK_extra_2      2.40 0.21
## BFIK_extra_3R    -0.35 0.23
## BFIK_extra_4     -0.21 0.21
## BFIK_extra_1R    -1.07 0.23

Summary statistics

for (i in seq_along(names(items))) {
  attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))
name label type type_options data_type value_labels optional item_order n_missing complete_rate min median max mean sd n_value_labels hist
BFIK_extra_2 Ich bin begeisterungsfähig und kann andere leicht mitreißen. rating_button 5 haven_labelled 1. 1: Trifft überhaupt nicht zu,
2. 2,
3. 3,
4. 4,
5. 5: Trifft voll und ganz zu,
NA. Item was never rendered for this user.		 0 6 0 1 1 4 5 4.178571 1.090483 6 ▁▁▁▁▁▇▁▇
BFIK_extra_3R Ich bin eher der “stille Typ”, wortkarg. rating_button 5 haven_labelled 5. 1: Trifft überhaupt nicht zu,
4. 2,
3. 3,
2. 4,
1. 5: Trifft voll und ganz zu,
NA. Item was never rendered for this user.		 0 14 0 1 1 4 5 3.750000 1.205696 6 ▂▂▁▅▁▇▁▇
BFIK_extra_4 Ich gehe aus mir heraus, bin gesellig. rating_button 5 haven_labelled 1. 1: Trifft überhaupt nicht zu,
2. 2,
3. 3,
4. 4,
5. 5: Trifft voll und ganz zu,
NA. Item was never rendered for this user.		 0 20 0 1 1 4 5 3.857143 1.112697 6 ▁▂▁▃▁▇▁▆
BFIK_extra_1R Ich bin eher zurückhaltend, reserviert. rating_button 5 haven_labelled 5. 1: Trifft überhaupt nicht zu,
4. 2,
3. 3,
2. 4,
1. 5: Trifft voll und ganz zu,
NA. Item was never rendered for this user.		 0 21 0 1 1 4 5 3.607143 1.196888 6 ▁▅▁▆▁▇▁▇

Scale: BFIK_neuro

Overview

Reliability: ωordinal [95% CI] = 0.66 [0.43;0.9].

Missing: 0.

old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height), 
                     old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)
if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
  likert_plot <- likert_from_items(items)
  if (!is.null(likert_plot)) {
    graphics::plot(likert_plot)
  }
}

knitr::opts_chunk$set(fig.height = old_height)
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
dist_plot <- plot_labelled(scale, scale_name, wrap_at)

choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
  suppressMessages( # ignore message about overwriting x axis
  dist_plot <- dist_plot +
        ggplot2::scale_x_continuous("values", 
                                breaks = breaks, 
                                labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
      ggplot2::expand_limits(x = range(breaks)))
  
}

dist_plot

Reliability details

for (i in seq_along(reliabilities)) {
  rel <- reliabilities[[i]]
  cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}
Reliability Indices
coefs <- x$scaleReliability$output$dat %>% 
  tidyr::gather(index, estimate) %>% 
  dplyr::filter(index != "n.items", index != "n.observations") %>% 
  dplyr::mutate(index = stringr::str_to_title(
    stringr::str_replace_all(index,
      stringr::fixed("."), " ")))

cis <- coefs %>% 
  dplyr::filter(stringr::str_detect(index, " Ci ")) %>% 
  tidyr::separate(index, c("index", "hilo"), sep = " Ci ") %>% 
  tidyr::spread(hilo, estimate)
if (nrow(cis)) {
  cis <- cis %>% dplyr::rename(
    `Lower 95% CI` = .data$Lo, `Upper 95% CI` = .data$Hi
  )
}

coefs_with_cis <- coefs %>% 
  dplyr::filter(!stringr::str_detect(index, " Ci ")) %>% 
    dplyr::left_join(cis, by = "index") %>% 
    dplyr::mutate(index = dplyr::if_else(index == "Glb", "Greatest Lower Bound", .data$index)) %>% 
    dplyr::arrange(!stringr::str_detect(index, "Omega")) %>% 
    dplyr::select(Index = .data$index, Estimate = .data$estimate)


pander::pander(coefs_with_cis)
Index Estimate
Omega 0.8191
Omega Psych Tot 0.7954
Omega Psych H 0.03191
Omega Ordinal 0.6649
Cronbach Alpha 0.7537
Greatest Lower Bound 0.8345
Alpha Ordinal 0.6023

Positive correlations: 3 out of 3 (100%)

Scatter matrix
print(x$scatterMatrix$output$scatterMatrix)

x$scatterMatrix$output$scatterMatrix <- no_md()

Detailed output

print(x)
## 
## Information about this analysis:
## 
##                  Dataframe: res$dat
##                      Items: BFIK_neuro_2R, BFIK_neuro_3, BFIK_neuro_4
##               Observations: 28
##      Positive correlations: 3 out of 3 (100%)
## 
## Estimates assuming interval level:
## 
##              Omega (total): 0.82
##       Omega (hierarchical): 0.03
##    Revelle's omega (total): 0.8
## Greatest Lower Bound (GLB): 0.83
##              Coefficient H: 0.98
##           Cronbach's alpha: 0.75
## Confidence intervals:
##              Omega (total): [0.71, 0.93]
##           Cronbach's alpha: [0.58, 0.92]
## 
## Estimates assuming ordinal level:
## 
##      Ordinal Omega (total): 0.66
##  Ordinal Omega (hierarch.): 0.64
##   Ordinal Cronbach's alpha: 0.6
## Confidence intervals:
##      Ordinal Omega (total): [0.43, 0.9]
##   Ordinal Cronbach's alpha: [0.34, 0.86]
## 
## Note: the normal point estimate and confidence interval for omega are based on the procedure suggested by Dunn, Baguley & Brunsden (2013) using the MBESS function ci.reliability, whereas the psych package point estimate was suggested in Revelle & Zinbarg (2008). See the help ('?scaleStructure') for more information.
## 
## Eigen values: 2.015, 0.723, 0.262
## Loadings:
##               PC1  
## BFIK_neuro_2R 0.670
## BFIK_neuro_3  0.863
## BFIK_neuro_4  0.907
## 
##                  PC1
## SS loadings    2.015
## Proportion Var 0.672
## 
##               vars  n mean   sd median trimmed  mad min max range skew kurtosis
## BFIK_neuro_2R    1 28 3.11 0.88      3    3.08 1.48   2   5     3 0.12    -1.14
## BFIK_neuro_3     2 28 3.07 1.27      3    3.08 1.48   1   5     4 0.08    -1.14
## BFIK_neuro_4     3 28 2.50 1.20      2    2.50 1.48   1   4     3 0.12    -1.60
##                 se
## BFIK_neuro_2R 0.17
## BFIK_neuro_3  0.24
## BFIK_neuro_4  0.23

Summary statistics

for (i in seq_along(names(items))) {
  attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))
name label type type_options data_type value_labels optional item_order n_missing complete_rate min median max mean sd n_value_labels hist
BFIK_neuro_2R Ich bin entspannt, lasse mich durch Stress nicht aus der Ruhe bringen. rating_button 5 haven_labelled 5. 1: Trifft überhaupt nicht zu,
4. 2,
3. 3,
2. 4,
1. 5: Trifft voll und ganz zu,
NA. Item was never rendered for this user.		 0 9 0 1 2 3 5 3.107143 0.8751417 6 ▆▁▇▁▁▇▁▁
BFIK_neuro_3 Ich mache mir viele Sorgen. rating_button 5 haven_labelled 1. 1: Trifft überhaupt nicht zu,
2. 2,
3. 3,
4. 4,
5. 5: Trifft voll und ganz zu,
NA. Item was never rendered for this user.		 0 15 0 1 1 3 5 3.071429 1.2744954 6 ▃▇▁▇▁▅▁▅
BFIK_neuro_4 Ich werde leicht nervös und unsicher. rating_button 5 haven_labelled 1. 1: Trifft überhaupt nicht zu,
2. 2,
3. 3,
4. 4,
5. 5: Trifft voll und ganz zu,
NA. Item was never rendered for this user.		 0 16 0 1 1 2 4 2.500000 1.2018504 6 ▆▁▇▁▁▂▁▇ 
missingness_report

Missingness report

if (length(md_pattern)) {
  if (knitr::is_html_output()) {
    rmarkdown::paged_table(md_pattern, options = list(rows.print = 10))
  } else {
    knitr::kable(md_pattern)
  }
}
items

Codebook table

export_table(metadata_table)
jsonld

JSON-LD metadata The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.

{
  "name": "codebook_data",
  "datePublished": "2020-05-02",
  "description": "The dataset has N=28 rows and 29 columns.\n0 rows have no missing values on any column.\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n|name          |label                                                                      | n_missing|\n|:-------------|:--------------------------------------------------------------------------|---------:|\n|session       |NA                                                                         |         0|\n|created       |user first opened survey                                                   |         0|\n|modified      |user last edited survey                                                    |         0|\n|ended         |user finished survey                                                       |         0|\n|expired       |NA                                                                         |        28|\n|BFIK_open_2   |__Ich bin tiefsinnig, denke gerne über Sachen nach.__                      |         0|\n|BFIK_agree_4R |__Ich kann mich schroff und abweisend anderen gegenüber verhalten.__       |         0|\n|BFIK_extra_2  |__Ich bin begeisterungsfähig und kann andere leicht mitreißen.__           |         0|\n|BFIK_agree_1R |__Ich neige dazu, andere zu kritisieren.__                                 |         0|\n|BFIK_open_1   |__Ich bin vielseitig interessiert.__                                       |         0|\n|BFIK_neuro_2R |__Ich bin entspannt, lasse mich durch Stress nicht aus der Ruhe bringen.__ |         0|\n|BFIK_consc_3  |__Ich bin tüchtig und arbeite flott.__                                     |         0|\n|BFIK_consc_4  |__Ich mache Pläne und führe sie auch durch.__                              |         0|\n|BFIK_consc_2R |__Ich bin bequem, neige zur Faulheit.__                                    |         0|\n|BFIK_agree_3R |__Ich kann mich kalt und distanziert verhalten.__                          |         0|\n|BFIK_extra_3R |__Ich bin eher der \"stille Typ\", wortkarg.__                               |         0|\n|BFIK_neuro_3  |__Ich mache mir viele Sorgen.__                                            |         0|\n|BFIK_neuro_4  |__Ich werde leicht nervös und unsicher.__                                  |         0|\n|BFIK_agree_2  |__Ich schenke anderen leicht Vertrauen, glaube an das Gute im Menschen.__  |         0|\n|BFIK_consc_1  |__Ich erledige Aufgaben gründlich.__                                       |         0|\n|BFIK_open_4   |__Ich schätze künstlerische und ästhetische Eindrücke.__                   |         0|\n|BFIK_extra_4  |__Ich gehe aus mir heraus, bin gesellig.__                                 |         0|\n|BFIK_extra_1R |__Ich bin eher zurückhaltend, reserviert.__                                |         0|\n|BFIK_open_3   |__Ich habe eine aktive Vorstellungskraft, bin phantasievoll.__             |         0|\n|BFIK_agree    |aggregate of 4 BFIK_agree items                                            |         0|\n|BFIK_open     |aggregate of 4 BFIK_open items                                             |         0|\n|BFIK_consc    |aggregate of 4 BFIK_consc items                                            |         0|\n|BFIK_extra    |aggregate of 4 BFIK_extra items                                            |         0|\n|BFIK_neuro    |aggregate of 3 BFIK_neuro items                                            |         0|\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.8.2).",
  "keywords": ["session", "created", "modified", "ended", "expired", "BFIK_open_2", "BFIK_agree_4R", "BFIK_extra_2", "BFIK_agree_1R", "BFIK_open_1", "BFIK_neuro_2R", "BFIK_consc_3", "BFIK_consc_4", "BFIK_consc_2R", "BFIK_agree_3R", "BFIK_extra_3R", "BFIK_neuro_3", "BFIK_neuro_4", "BFIK_agree_2", "BFIK_consc_1", "BFIK_open_4", "BFIK_extra_4", "BFIK_extra_1R", "BFIK_open_3", "BFIK_agree", "BFIK_open", "BFIK_consc", "BFIK_extra", "BFIK_neuro"],
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "variableMeasured": [
    {
      "name": "session",
      "@type": "propertyValue"
    },
    {
      "name": "created",
      "description": "user first opened survey",
      "@type": "propertyValue"
    },
    {
      "name": "modified",
      "description": "user last edited survey",
      "@type": "propertyValue"
    },
    {
      "name": "ended",
      "description": "user finished survey",
      "@type": "propertyValue"
    },
    {
      "name": "expired",
      "@type": "propertyValue"
    },
    {
      "name": "BFIK_open_2",
      "description": "__Ich bin tiefsinnig, denke gerne über Sachen nach.__",
      "value": "1. 1: Trifft überhaupt nicht zu,\n2. 2,\n3. 3,\n4. 4,\n5. 5: Trifft voll und ganz zu,\nNA. Item was never rendered for this user.",
      "maxValue": 5,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "BFIK_agree_4R",
      "description": "__Ich kann mich schroff und abweisend anderen gegenüber verhalten.__",
      "value": "5. 1: Trifft überhaupt nicht zu,\n4. 2,\n3. 3,\n2. 4,\n1. 5: Trifft voll und ganz zu,\nNA. Item was never rendered for this user.",
      "maxValue": 5,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "BFIK_extra_2",
      "description": "__Ich bin begeisterungsfähig und kann andere leicht mitreißen.__",
      "value": "1. 1: Trifft überhaupt nicht zu,\n2. 2,\n3. 3,\n4. 4,\n5. 5: Trifft voll und ganz zu,\nNA. Item was never rendered for this user.",
      "maxValue": 5,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "BFIK_agree_1R",
      "description": "__Ich neige dazu, andere zu kritisieren.__",
      "value": "5. 1: Trifft überhaupt nicht zu,\n4. 2,\n3. 3,\n2. 4,\n1. 5: Trifft voll und ganz zu,\nNA. Item was never rendered for this user.",
      "maxValue": 5,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "BFIK_open_1",
      "description": "__Ich bin vielseitig interessiert.__",
      "value": "1. 1: Trifft überhaupt nicht zu,\n2. 2,\n3. 3,\n4. 4,\n5. 5: Trifft voll und ganz zu,\nNA. Item was never rendered for this user.",
      "maxValue": 5,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "BFIK_neuro_2R",
      "description": "__Ich bin entspannt, lasse mich durch Stress nicht aus der Ruhe bringen.__",
      "value": "5. 1: Trifft überhaupt nicht zu,\n4. 2,\n3. 3,\n2. 4,\n1. 5: Trifft voll und ganz zu,\nNA. Item was never rendered for this user.",
      "maxValue": 5,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "BFIK_consc_3",
      "description": "__Ich bin tüchtig und arbeite flott.__",
      "value": "1. 1: Trifft überhaupt nicht zu,\n2. 2,\n3. 3,\n4. 4,\n5. 5: Trifft voll und ganz zu,\nNA. Item was never rendered for this user.",
      "maxValue": 5,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "BFIK_consc_4",
      "description": "__Ich mache Pläne und führe sie auch durch.__",
      "value": "1. 1: Trifft überhaupt nicht zu,\n2. 2,\n3. 3,\n4. 4,\n5. 5: Trifft voll und ganz zu,\nNA. Item was never rendered for this user.",
      "maxValue": 5,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "BFIK_consc_2R",
      "description": "__Ich bin bequem, neige zur Faulheit.__",
      "value": "5. 1: Trifft überhaupt nicht zu,\n4. 2,\n3. 3,\n2. 4,\n1. 5: Trifft voll und ganz zu,\nNA. Item was never rendered for this user.",
      "maxValue": 5,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "BFIK_agree_3R",
      "description": "__Ich kann mich kalt und distanziert verhalten.__",
      "value": "5. 1: Trifft überhaupt nicht zu,\n4. 2,\n3. 3,\n2. 4,\n1. 5: Trifft voll und ganz zu,\nNA. Item was never rendered for this user.",
      "maxValue": 5,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "BFIK_extra_3R",
      "description": "__Ich bin eher der \"stille Typ\", wortkarg.__",
      "value": "5. 1: Trifft überhaupt nicht zu,\n4. 2,\n3. 3,\n2. 4,\n1. 5: Trifft voll und ganz zu,\nNA. Item was never rendered for this user.",
      "maxValue": 5,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "BFIK_neuro_3",
      "description": "__Ich mache mir viele Sorgen.__",
      "value": "1. 1: Trifft überhaupt nicht zu,\n2. 2,\n3. 3,\n4. 4,\n5. 5: Trifft voll und ganz zu,\nNA. Item was never rendered for this user.",
      "maxValue": 5,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "BFIK_neuro_4",
      "description": "__Ich werde leicht nervös und unsicher.__",
      "value": "1. 1: Trifft überhaupt nicht zu,\n2. 2,\n3. 3,\n4. 4,\n5. 5: Trifft voll und ganz zu,\nNA. Item was never rendered for this user.",
      "maxValue": 5,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "BFIK_agree_2",
      "description": "__Ich schenke anderen leicht Vertrauen, glaube an das Gute im Menschen.__",
      "value": "1. 1: Trifft überhaupt nicht zu,\n2. 2,\n3. 3,\n4. 4,\n5. 5: Trifft voll und ganz zu,\nNA. Item was never rendered for this user.",
      "maxValue": 5,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "BFIK_consc_1",
      "description": "__Ich erledige Aufgaben gründlich.__",
      "value": "1. 1: Trifft überhaupt nicht zu,\n2. 2,\n3. 3,\n4. 4,\n5. 5: Trifft voll und ganz zu,\nNA. Item was never rendered for this user.",
      "maxValue": 5,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "BFIK_open_4",
      "description": "__Ich schätze künstlerische und ästhetische Eindrücke.__",
      "value": "1. 1: Trifft überhaupt nicht zu,\n2. 2,\n3. 3,\n4. 4,\n5. 5: Trifft voll und ganz zu,\nNA. Item was never rendered for this user.",
      "maxValue": 5,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "BFIK_extra_4",
      "description": "__Ich gehe aus mir heraus, bin gesellig.__",
      "value": "1. 1: Trifft überhaupt nicht zu,\n2. 2,\n3. 3,\n4. 4,\n5. 5: Trifft voll und ganz zu,\nNA. Item was never rendered for this user.",
      "maxValue": 5,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "BFIK_extra_1R",
      "description": "__Ich bin eher zurückhaltend, reserviert.__",
      "value": "5. 1: Trifft überhaupt nicht zu,\n4. 2,\n3. 3,\n2. 4,\n1. 5: Trifft voll und ganz zu,\nNA. Item was never rendered for this user.",
      "maxValue": 5,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "BFIK_open_3",
      "description": "__Ich habe eine aktive Vorstellungskraft, bin phantasievoll.__",
      "value": "1. 1: Trifft überhaupt nicht zu,\n2. 2,\n3. 3,\n4. 4,\n5. 5: Trifft voll und ganz zu,\nNA. Item was never rendered for this user.",
      "maxValue": 5,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "BFIK_agree",
      "description": "aggregate of 4 BFIK_agree items",
      "@type": "propertyValue"
    },
    {
      "name": "BFIK_open",
      "description": "aggregate of 4 BFIK_open items",
      "@type": "propertyValue"
    },
    {
      "name": "BFIK_consc",
      "description": "aggregate of 4 BFIK_consc items",
      "@type": "propertyValue"
    },
    {
      "name": "BFIK_extra",
      "description": "aggregate of 4 BFIK_extra items",
      "@type": "propertyValue"
    },
    {
      "name": "BFIK_neuro",
      "description": "aggregate of 3 BFIK_neuro items",
      "@type": "propertyValue"
    }
  ]
}`